2015 sibgrapi-contactlenses

An Approach toIris Contact Lens

Detection

P. Silva1, E. Luz1,R. Baeta1, H.Pedrini2, A. X.Falcao2, D.Menotti1,3

Introduction

Iris

Contact LensDetection

Objectives

Background

ConvolutionalNetworks

Krizhevsky’sFramework

Proposed TrainingMethodology

Experiments

Databases

Architecture

Results

Conclusion andFuture Works

An Approach to Iris Contact Lens Detectionbased on Deep Image Representations

P. Silva1, E. Luz1, R. Baeta1, H. Pedrini2, A. X. Falcao2,D. Menotti1,3

Department of Computing, UFOP, Ouro Preto, BrazilInstitute of Computing, UNICAMP, Campinas, BrazilDepartment of Informatics, UFPR, Curitiba, Brazil

[email protected] / [email protected]

SIBGRAPI 2015IEEE Conference on Graphics, Patterns and Images

TS5 - Selected papersSalvador (BA), Brazil - August 27th, 2015

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


Overview

IntroductionIrisContact Lens DetectionObjectives

BackgroundConvolutional NetworksKrizhevsky’s Framework

Proposed Training Methodology

ExperimentsDatabasesArchitecture

Results

Conclusion and Future Works

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


IntroductionIris I

I Richness of details:I freckles (sardas);I furrows (sulcos).

Figure : Example of an Iris image

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


IntroductionIris II

I Depends on environment in which embryo develops;(baby’s pregnancy - 9 months of age)

I There is no genetic standard(even twins have different irises &right and left eye are different);

I Aging vs Changing biometrics;

I Uniqueness: 1072.

most secure trait for biometric systems

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


IntroductionContact Lens Detection & Spoofing

I Attack ways to biometric systems:I Direct (synthetic samples);I Indirect (hardware, Software).

I Spoofing (false biometric as a legitimate);

I Attacks to biometric systems based on iris:I Cosmetic/Colored Lenses;I Transcript/Soft Lenses;I “Naked” eye (no lenses).

I Transcript lenses impact in the iris recognition system

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


IntroductionObjectives

I Learn deep representations to discriminate:I Textured (colored) contact lenses;I Soft (transparent) contact lenses;I Non (nude) contact lenses.

I Learn features direct from data;

I Create a fast and reliable method;

I Validate our method using public databases.

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


Background

I Deep Learning:I Convolutional Networks (CN);

I Bio-inspired (how to learn weights???).

I Krizhevsky’s (& Hinton) Framework(CUDA-convnet - ImageNet (NIPS 2012))

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


BackgroundConvolutional Networks I

I Pipeline (of a single and not deep layer):

1. Convolution;2. Activation;3. Subsampling (pooling);4. Normalization.

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


BackgroundConvolutional Networks II

I Filter bank of convolution:I Multiband filters.I Window size (n × n);

Figure : Convolution process

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


BackgroundConvolutional Networks III

I Filter bank of convolution;

I Linear Activation;

Figure : Types of activation

Figure : Activation used

Ji (p) = max(Ji (p), 0)

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


BackgroundConvolutional Networks IV

I Pooling / subsampling:I Translation to invariance;I Window size (n × n);I Max-pooling, average-pooling, α-pooling.

Figure : Example of subsampling

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


BackgroundConvolutional Networks V

I Normalization;I Increase the quality of the feature vector;I Enhance the discriminative capability;I Windowed operation among filter responses.

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


BackgroundConvolutional Networks VI

I Pipeline:I Filter bank of convolution;I Linear activation;I Pooling / subsampling;I Normalization;

I Parameteres:I Filter Size (receptive field);I Number of filter per layer;I Activation function;I Pooling and Normalization window size.

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


BackgroundConvolutional Networks VII

I Pipeline:I Filter bank of convolution;I Linear Activation;I Pooling / Subsampling and Normalization;

I Parameters;

I Types of CN Learning:I Architecture Optimization

(Pinto, Bergstra, Cox & others);I Filter/Weights Learning

(Krizhevsky’s, Hinton, Lecun & others).

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


BackgroundKrizhevsky’s Framework

I CUDA-convnet library in C++/CUDA;

I Fully connected layers with learning by softmaxregression.

I Training methodology for the CIFAR database1:

1. Train using three batches and generalization /specialization tests in the fourth batch, using a learningrate of 10−3 for 100 epochs;

2. Train using the four batches for more 40 epochs;3. Reduce the learning rate by 10 and train for more 10

epochs;4. Reduce the learning rate again by 10 and train for more

10 epochs;

1https://code.google.com/p/cuda-convnet/wiki/Methodology

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https://code.google.com/p/cuda-convnet/wiki/Methodology


Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


Proposed Training Methodology I

I Network Architecture:I Number of convolutoin layers 1, 2, 3;I Use or not of normalization operation;I Number of convolution filters 16, 32, 64;

I Input image size :I 64× 64, 128× 128, 256× 256;I Square images.

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


Proposed Training Methodology III Crop borders:

I 64x64 → {2, 4, 6, 8};I 128x128 → {4, 8, 12, 16};I 256x256 → {8, 16, 24, 32};I Square images.

I Annotation of iris (background):

I 0% (no background);I 10%;I 20%;

I 30%;I 40%.

Figure : Example of image17 / 32


Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


ExperimentsDatabases

I Notre Dame Contact Lens Database (NDCL)I 640× 480 gray images;I LG4000 and AD100 sensors;I ID of each iris/image;I Eye (left and right);I Gender;I Race;I Contact lens type (Soft/Textured/Non);I Coordinates of pupil and iris centers and their

corresponding radii.

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results



I Notre Dame Contact Lens Database (NDCL)I IIIT-D Contact Lens Iris Database (IIIT-D)

I 640× 480 gray images;I Cogent Dual and VistaFA2E single sensors;I Does not have pupils and iris information (no

segmentation).

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results



NDCL IIIT-DAD100 LG4000 Cogent Vista

T

S

N

Figure : Examples in the NDCL and IIIT-D databases. Cols: (1)NDCL-AD100, (2) NDCL-LG4000, (3) IIIT-D-Cogent, and(4)IIIT-D-Vista. Rows: (1) Textured, (2) Soft, and (3) Non lenses.

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results



Base Sensor# Train # Test # Full

Text. Trans. Sem Total Text. Trans. Sem Total Text. Trans. Sem Total

NDCLAD100 200 200 200 600 100 100 100 300 300 300 300 900LG4000 1000 1000 1000 3000 400 400 400 1200 1400 1400 1400 4200Multi 1200 1200 1200 3600 500 500 500 1500 1700 1700 1700 5100

IIIT-DCogent 589 569 563 1721 613 574 600 1787 1202 1143 1163 3508Vista 535 500 500 1535 530 510 500 1540 1065 1010 1000 3075Multi 1124 1069 1063 3256 1143 1084 1100 3327 2267 2153 2163 6583

Table : Main characteristics of the databases used here.

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


ExperimentsSpoofnet

Figure : Topology of the initial network (Spoofnet) (Menotti et al.- IEEE T. Information Forensics and Security, 2015)

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


ExperimentsArchitecture evaluation – number of layers vs number of filters

Sensor N. filters CR N. filters CR

AD100

16 72.33 16-16-16 73.6732 68.67 16-16-32 76.0064 70.00 16-16-64 77.00

16-16 75.67 16-32-16 72.3316-32 75.00 16-32-32 76.3316-64 74.67 16-32-64 71.0032-32 76.00 32-32-16 75.0032-64 76.00 32-32-64 79.67

LG4000

16 79.50 16-16-16 77.5932 77.34 16-16-32 83.3464 80.84 16-16-64 81.17

16-16 84.34 16-32-16 82.9216-32 84.82 16-32-32 81.7516-64 84.17 16-32-64 76.9232-32 85.59 32-32-16 81.3432-64 85.00 32-32-64 83.75

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


ExperimentsSource evaluation – input image size vs crop borders vs backgroundaddition (%)

Input image size & crop borders

SensorBackground 64 × 64 128 × 128 256 × 256addition (%) 2 4 6 8 4 8 12 16 8 16 24 32

AD100

0 74.67 73.67 71.00 71.00 74.00 78.00 70.67 70.00 70.33 70.67 71.33 63.3310 74.67 78.33 74.00 73.33 73.33 76.00 72.67 65.33 71.33 73.67 68.00 62.3320 71.33 76.67 76.00 67.33 69.67 75.33 76.33 68.00 71.33 71.00 73.00 68.3330 69.00 70.00 72.67 75.00 68.33 72.33 73.00 75.33 67.33 70.00 72.00 67.0040 66.33 69.67 72.67 69.67 73.33 71.67 71.00 68.33 66.67 69.67 75.67 68.33

LG4000

0 82.50 81.92 82.75 82.08 84.25 83.83 84.17 82.08 77.25 76.50 76.00 77.0010 83.25 86.00 84.25 82.75 84.58 84.58 85.25 82.92 72.25 75.58 76.25 75.0820 81.25 82.83 84.08 80.58 84.75 84.83 85.58 84.33 72.17 74.92 75.83 73.5830 82.00 81.92 82.50 80.00 83.42 82.83 84.33 84.42 71.08 70.42 74.50 71.3340 80.25 81.42 81.50 82.08 82.17 82.92 84.92 82.67 68.00 70.58 72.08 71.08

Table : Evaluating the impact in the classification rate based onparameters affecting the input to network.

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


ExperimentsCLDnet

Figure : Topology of the final network (CLDnet)

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Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


ResultsAcronyms

I PM - Proposed Method.

I SOTA - State-of-the-Art.

I 2nd - “Second” SOTA.

I N - Classification rate for Non lenses;

I T - Classification rate for Textured lenses;

I S - Classification rate for Soft lenses;

I O - Overall Classification rate.

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Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


ResultsIntra-sensor evaluations (direct)

SensorsNDCL IIIT-D

AD100 LG4000 Cogent VistaPM SOTA PM SOTA PM SOTA 2nd PM SOTA 2nd

N 73.00 81.00 84.50 76.21 35.50 66.83 59.73 60.80 76.21 49.49T 97.00 100.00 99.75 91.62 73.00 94.91 91.87 55.88 91.62 99.42S 65.00 52.00 73.75 67.52 98.21 56.66 52.84 98.30 67.52 59.32O 78.33 77.67 86.00 80.04 69.05 73.01 68.57 72.08 80.04 69.84

Table : CRs for the Intra-sensor evaluation.

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Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


ResultsInter-sensor evaluation (cross)

SensorsNDCL IIIT-D

Training AD100 LG4000 Cogent VistaTesting LG4000 AD100 Vista Cogent

PM SOTA PM SOTA PM SOTA PM SOTAN 75.00 62.25 80.00 74.00 6.00 62.10 48.67 65.99T 94.00 88.50 97.00 93.00 89.61 92.95 38.15 80.81S 65.00 29.50 49.00 17.00 45.47 75.44 42.25 48.31O 78.00 60.08 75.33 61.33 45.51 77.79 43.08 65.29

Table : CRs for the Inter-sensor evaluation.

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Introduction

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Background




Experiments

Databases

Architecture

Results


ResultsMulti-sensor evaluation (fusion)

DatabasesNDCL IIIT-D

PM SOTA PM SOTAN 77.40 72.60 47.55 62.14T 99.60 97.00 61.07 94.74S 71.40 50.00 97.99 61.63O 82.80 73.20 69.28 72.96

Table : CRs for Multi-sensor evaluation.

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Introduction

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Background




Experiments

Databases

Architecture

Results


ResultsRuntime

I GPU based framework:I Intel i7 Core CPUsI NVIDIA GPUs (GTX Titan Black, Titan X, Tesla K40)

Resolution Time (min)256× 256 172128× 128 49

64× 64 11

Table : Training time for the LG4000 sensor.

I Classification (testing) time:I “The SDK for Jetpac’s iOS Deep Beli...”I iPhone 5S - classify an 256× 256 image in 300 ms

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


Conclusion and Future Works

I Conclusion:I Discriminative feature vector learned from data by Deep

Learning techniques (Convolutional networks);I State-of-the-art results for the NDCL database;I Comparable results for the IIIT-D database;I The iris location/segmentation impacts the

classification results;I Number of samples per class vs CN Learning

(AD100 and LG4000);

I Future works:I Architecture optimization

(automatic definition of network topology);I Manual iris annotation of the IIIT-D database.I Proposition of an iris segmentation approach

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Detection


Introduction

Iris


Objectives

Background




Experiments

Databases

Architecture

Results


That’s all folks!!!

Question???

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2015 sibgrapi-contactlenses

Science